A novel framework integrating GeoAI and human perceptions to estimate walkability in Wuhan, China.

Evidence shows enhanced walking environment promotes overall physical activities and further alleviates the risk of chronic diseases and mental disorders. Current walkability research is limited by traditional GIS methods that fail to capture micro-level details and human perceptions. Additionally, existing image segmentation techniques return low accuracy when extracting complex street environment features. Therefore, we developed a hierarchical evaluation framework for urban walkability with high precision image segmentation techniques, and subjective measurements on four first-level indicators (greenness, openness, crowding, safety) and their corresponding second-level indicators. An entropy weight method was constructed to quantify the indicators based on questionnaires from 120 volunteers. Furthermore, we developed Detail-Strengthened High-Resolution Network (DS-HRNet), a deep learning model that demonstrates a 15% improvement in street scene segmentation performance compared to existing models. Using the newly developed deep learning model, we analyzed 113,900 street view images in central Wuhan City, China. Our walkability results revealed spatial heterogeneity across the city, characterized by substantial disparities between adjacent areas, particularly in commercial areas. Subsequent socioeconomic analysis demonstrated that better walkability exists in areas of higher socioeconomic status but lower proportion of non-local residents. This walkability inequality may further lead to health disparities through its influence on physical activity and social interaction.